From adoption to impact: Putting the DORA AI Capabilities Model to work

The 2025 State of AI-assisted Software Development report revealed a critical truth: AI is an amplifier. It magnifies the strengths of high-performing organizations and the dysfunctions of struggling ones.

While AI adoption is now near-universal, with 90% of developers using it in their daily workflows, success is not guaranteed. Our cluster analysis of nearly 5,000 technology professionals reveals significant variation in team performance: Not everyone experiences the same outcomes from adopting AI. 

From this disparity, we can conclude that how they are using AI is a critical factor. We wanted to understand the particular capabilities and conditions that enable teams to achieve positive outcomes, leading us to develop the DORA AI Capabilities Model report. 

This companion guide to the 2025 DORA Report is designed to help you navigate our new reality. It provides actionable strategies, implementation tactics, and measurement frameworks to help technology leaders build an environment where AI thrives.

Seven capabilities that amplify success

Successfully using AI requires cultivating your technical and cultural environment. From the same set of respondents who participated in the 2025 DORA survey, we identified seven foundational capabilities that are proven to amplify the positive impact of AI on organizational performance:

1. Clear and communicated AI stance: Ambiguity creates risk. A clear policy provides the psychological safety developers need to experiment effectively.
2. Healthy data ecosystems: AI is only as good as the data it learns from. Investing in high-quality, accessible, and unified internal data significantly amplifies AI’s benefits.
3. AI-accessible internal data: This involves “context engineering,” moving beyond simple prompts to securely connect AI tools to your internal documentation and codebases.
4. Strong version control practices: As AI increases the volume and velocity of code generation, version control becomes your critical safety net. Frequent commits and robust rollback capabilities are essential for maintaining stability in an AI-assisted world.
5. Working in small batches: AI can easily generate massive blocks of code, which are hard to review and test. Enforcing the discipline of small batches counteracts this risk, ensuring that speed translates to product performance rather than instability.
6. User-centric focus: Speed is irrelevant if you are moving in the wrong direction. Adopting AI tools can actually harm teams that lack a user-centric focus. Keeping user needs as your North Star is essential for guiding AI-assisted development.
7. Quality internal platforms: A platform provides the automated, secure “paved roads” that allow AI benefits to scale across the organization. It prevents individual productivity gains from being lost to downstream bottlenecks.

Where to start: Assessing your team

Every organization starts their AI journey differently. To help you prioritize, this report introduces seven distinct team archetypes derived from our cluster analysis. These profiles range from “harmonious high-achievers,” who excel in both performance and well-being, to teams facing “foundational challenges” or those stuck in a “legacy bottleneck,” where unstable systems undermine morale.

Identifying the profile that best matches your team can help pinpoint the most impactful interventions. For example, a “high impact, low cadence” team might prioritize automation to improve stability, while a team “constrained by process” might focus on reducing friction through a better AI stance.

Digging deeper with Value Stream Mapping

Once you understand your team’s profile, how do you direct your efforts? The report includes a step-by-step facilitation guide for running a Value Stream Mapping (VSM) exercise.

VSM acts as an AI force multiplier. By visualizing your flow from idea to customer, you can identify where work waits and where friction exists. This ensures that the efficiency gains from AI aren’t just creating local optimizations that pile up work downstream, but are instead channeled into solving system-level constraints.

Get better at getting better

AI adoption is an organizational transformation. The greatest returns come not from the tools themselves, but from investing in the foundational systems that enable them.

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